US 7676043 B1 Abstract Bandwidth expansion for audio signals by frequency band translations plus adaptive gains to create higher frequencies; use of a common channel for both stereo channels limits computational complexity. Adaptive cut-off frequency determination by power spectrum curve analysis, and bass expansion by both fundamental frequency illusion and equalization.
Claims(6) 1. A method of stereo audio bandwidth expansion, comprising the steps of:
(a) amplitude modulating an average of left channel and right channel audio inputs;
(b) highpass filtering the results of step (a) where the highpass filter has a low cutoff frequency approximating an upper cutoff frequency for said left and right channel audio inputs;
(c) computing a left channel gain and a right channel gain;
(d) multiplying the results of step (b) by said left channel gain and adding the product to said left channel audio input to give a bandwidth expanded left channel audio output; and
(e) multiplying the results of step (b) by said right channel gain and adding the product to said right channel audio input to give a bandwidth expanded right channel audio output.
2. The method of
3. The method of
4. The method of
5. A stereo audio bandwidth expander, comprising:
(a) an averager with inputs coupled to left and right channel audio inputs;
(b) an amplitude modulator plus first highpass filter coupled to an output of said averager;
(c) a left channel highpass filter with input coupled to said left channel audio input and output coupled to a left channel gain determiner;
(d) a right channel highpass filter with input coupled to said right channel audio input and output coupled to a right channel gain determiner;
(e) a second highpass fiter with input coupled to said output of said averager and output coupled to both said left channel gain determiner and said right channel gain determiner;
(f) a left channel amplifier with inputs coupled to outputs of said first highpass filter and to said left channel gain determiner;
(g) a right channel amplifier with inputs coupled to outputs of said first highpass filter and to said right channel gain determiner;
(h) a left channel output combiner with inputs coupled to said left channel audio input and to an output of said left channel amplifier; and
(i) a right channel output combiner with inputs coupled to said right channel audio input and to an output of said right channel amplifier.
6. The stereo audio bandwidth expander of
Description This application claims priority from provisional applications Nos. 60/657,234, filed Feb. 28, 2005, 60/749,994, filed Dec. 13, 2005, and 60/756,099, filed Jan. 4, 2006. Co-assigned, copending patent application No. 60/660,372, filed Mar. 9, 2005 discloses related subject matter. The present invention relates to digital signal processing, and more particularly to audio frequency bandwidth expansion. Audio signals sometimes suffer from inferior sound quality. This is because their bandwidths have been limited due to the channel/media capacity of transfer/storage systems. For example, cut-off frequencies are set at about 20 kHz for CD, 16 kHz for MP3, 15 kHz for FM radio, and even lower for other audio systems whose data rate capability are poorer. At playback time, it is beneficial to recover high frequency components that have been discarded in such systems. This processing is equivalent to expanding an audio signal bandwidth, so it can be called bandwidth expansion (BWE); see On the other hand, time domain processing for BWE has been proposed in which high frequency components are synthesized by using amplitude modulation (AM) and extracted by using a high-pass filter. This system performs the core part of high frequency synthesis in time domain and is time domain alias-free. Another property employed is to estimate the cut-off frequency of input signal, on which the modulation amount and the cut-off frequency of the high-pass filter can be determined in run-time depending on the input signal. BWE algorithms work most efficiently when the cut-off frequency is known beforehand. However, it varies depending on signal content, bit-rate, codec, and encoder used. It can vary even within a single stream along with time. Hence, a run-time cut-off frequency estimator, as shown in Another bandwidth problem occurs at low frequencies: bass loudspeakers installed in electric appliances such as flat panel TV, mini-component, multimedia PC, portable media player, cell-phone, and so on cannot reproduce bass frequencies efficiently due to their limited dimensions relative to low frequency wavelengths. With such loudspeakers, the reproduction efficiency starts to degrade rapidly from about 100-300 Hz depending on the loudspeakers, and almost no sound is excited below 40-100 Hz; see The present invention provides audio bandwidth expansion with adaptive cut-off frequency detection and/or a common expansion for stereo signals and/or even-odd harmonic generation for part of low frequency expansion. 1. Overview Preferred embodiment methods include audio bandwidth extensions at high and/or low frequencies. Preferred embodiment high-frequency bandwidth expansion (BWE) methods include amplitude modulation and a high-pass filter for high frequency synthesis which reduces computation by making use of an intensity stereo processing in case of stereo signal input. Another BWE preferred embodiment estimates the level of high frequency components adaptively; this enables smooth transition in spectrum from original band-limited signals to synthesized high frequencies with a more natural sound quality. Further preferred embodiments provide for the run-time creation of the high-pass filter coefficients, use of windowed sinc functions that requires low computation with much smaller look-up table size for ROM. This filter is designed to have linear phase, and thus is free from phase distortion. And the FIR filtering operation is done in frequency domain using the overlap-save method, which saves significant amount of computation. Some other operations including the AM operation are also converted to frequency domain processing so as to minimize the number of FFT operations. In particular, a preferred embodiment method first identifies a cut-off frequency, as the candidate, with adaptive thresholding of the input power spectrum. The threshold is adaptively determined based on the signal level and the noise floor that is inherent in digital (i.e., quantized) signals. The use of the noise floor helps discriminate the presence of high frequencies in input signals. To verify the candidate cut-off frequency, the present invention then detects the spectrum envelope around the candidate. If no ‘drop-off’ is found in the spectrum envelope, the candidate will be treated as a false cut-off and thus discarded. In that case, the cut-off frequency will be identified as the Nyquist frequency F Preferred embodiment systems perform preferred embodiment methods with any of several types of hardware: digital signal processors (DSPs), general purpose programmable processors, application specific circuits, or systems on a chip (SoC) which may have multiple processors such as combinations of DSPs, RISC processors, plus various specialized programmable accelerators; see 2. Single-channel AM-based BWE with Adaptive Signal Level Estimation Preferred embodiment methods and devices provide for stereo BWE using a common extension signal. Thus, initially consider preferred embodiment BWE for a single channel system, this will be the baseline implementation for the preferred embodiment stereo-channel BWE. We adopt the AM-based BWE method due to its good sound quality and lower computation complexity. In the figure, u As shown in Before being added to x(n), the level of u In Then G(n) is determined by
From its definition, G(n) can be seen to be a rough estimation of the energy transition of |X(f)| for f in the interval F Preferred embodiment methods estimate the cut-off frequency F The input sequence x(n) is assumed to be M-bit linear pulse code modulation (PCM), which is a very general and reasonable assumption in digital audio applications. The frequency spectrum of x(n) accordingly has the so-called noise floor originating from quantization error as shown in Suppose that x(n) was obtained through quantization of the original signal u(n) in which q(n)=x(n)−u(n) is the quantization error, and the quantization step size is
From Parseval's theorem, the following relation holds:
As shown in block diagram Let x Define the peak power spectrum of the m-th frame, P After the peak power spectrum is obtained, the candidate cut-off frequency k _{X}+ _{Q})/2where the calligraphic letters represent the decibel value of the corresponding power variable as =10 log _{10 } P d presents an illustrative explanation of the adaptive thresholding. From the expression “mean peak power”, one might think that P_{X }should be located lower than depicted in the figure as the mean magnitude of P(k) for [K_{1}, K_{2}] will be slightly above T in the figure. However, P_{X }is not the mean magnitude, i.e., the mean in the decibel domain, but the physical mean power as defined by the sum over [K_{1}, K_{2}]. As a result, the threshold T will be placed between the signal level and the noise floor so that it will be adapted suitably to the signal level.
It must be noted that, even if there is no actual cut-off in P(k), the above method will identify a certain k In order to see if there is the actual cut-off at k _{L2}− _{L1})/D _{L} b _{L}=(K _{L2} _{L1} −K _{L1} _{L2})/D _{L} where _{L1}, _{L2 }are again decibel values of P_{L1}, P_{L2}.
Similarly, for the envelope above k In the preferred embodiment method, the candidate cut-off frequency k _{b }is a threshold. The condition indicates that there should be a drop-off larger than _{b }(dB) at k_{c}′ so that the candidate can be considered as the true cut-off frequency.
There are many other possible ways to verify the candidate cut-off frequency k >b _{H} This condition means that the offsets should be on the expected side of the threshold. Even more sophisticated and robust criteria may be considered using the slopes a _{L }and a_{H}.
5. BWE in Time Domain The high-pass filter coefficients H Our FIR filter design method is similar to that presented in cross-reference patent application No. 60/660,372, which is based on the well-known windowed sinc function. The impulse response h The FIR filter derived above doesn't satisfy causality; that is, there exists m such that h FIR filtering is a convolution with the impulse response function; and convolution transforms into pointwise multiplication in the frequency domain. Consequently, a popular alternative formulation of FIR filtering includes first transform (e.g., FFT) a block of the input signal and the impulse response to the frequency domain, next multiply the transforms, and lastly, inverse transform (e.g, IFFT) the product back to the time domain. Due to the frame-based processing, the cut-off frequency estimation can be done each frame, not for each input sample. Hence update of the high-pass filter becomes to be done less frequently. However, as is often the case, this causes no quality degradation because the input signal can be assumed to be stationary during a certain amount of duration, and the cut-off frequency is expected to change slowly. For the r-th frame, the DFT (FFT implementation) of x The AM operation is applied to x Also note that, the use of overlapped frames requires another condition to be satisfied on the output frame size R. The cosine weight in the modulation for overlapped input signals in successive frames has to be the same values. Otherwise the same input signal in different frames is weighted by different cosine weights, which causes perceptual distortion around output frame boundaries. Since
Then we convert the operation modulation to the frequency domain. Again with capitals denoting transforms of lower case:
Now apply the overlap-save method to implement the time domain FIR convolution at the end of section 5. Let the r-th frame of the output from the high-pass filter be u Now let V Here we explain our method to calculate the DFT of the filter coefficients, H It is well known that the time domain point-wise multiplication is equivalent to circular convolution of the DFT coefficients. Let H The order of the high-pass filter, which has been set at 2L=N−R in the preferred embodiment method, can be further examined. In general, we hope to design a long filter that has better cut-off characteristic. However, due to the behavior of circular convolution of the overlap-save method, illegally long order of filter results in time domain alias. See Preceding section 4 provided the method that estimates frame-varying cut-off frequency k Comparison of X Since the preferred embodiment frequency domain method for BWE is much more complicated than that the time domain method, we summarize the steps of the procedure. (1) Receive R input samples and associate an N-sample frame overlapped with the previous ones. The overlap length N−R has to be N−R=2L, where 2L is the order of high-pass filter H (2) The N sample input signal is processed with FFT to obtain X (3) X (4) Using X (5) X (6) U (7) U (8) The gain g(n) is determined as in section 3, and applied to the high frequency components u (9) The signal u 7. Bass Expansion The bass boost filter is intended to equalize the loudspeaker of interest for the higher bass frequencies f The preferred embodiment harmonics generator generates integral-order harmonics of the lower bass frequencies f The peak detector ‘Peak’ works as an envelope estimator. Its output is used to eliminate dc (direct current) component of the full wave rectified signal, and to determine the clipping threshold. The following paragraphs describe the peak detection and the method of generating harmonics efficiently using the detected peak. The peak detector detects peak absolute value of the input signal s(n) during each half-wave. A half-wave means a section between neighboring zero-crossings. -
- sgn=1;
- maxima=0;
- p(−1)=0;
- for (n=0; ; n++) {
- maxima=max(maxima, fabs(s(n)));
- if (sgn ★ s(n)<0) {
- p(n)=maxima;
- maxima=0;
- sgn=−sgn;
- {
- else {
- p(n)=p(n−1);
- }
- }
To generate even-order harmonics h The frequency characteristics of h Let f(t) be a periodic function of period 2π. Then, the Fourier series of f(t) is given by
To generate higher harmonics of odd-order, the preferred embodiment clips the input signal s(n) at a certain threshold T(T>0) as follows: The Fourier coefficients of a unit sinusoidal, sin t, clipped with the threshold T=sin θ are given by
The similarity in the decay rate is suggested as follows. When the threshold parameter θ is set to θ=π/4, the magnitude of the k≠1, odd Fourier coefficients become
We implemented and tested the proposed method in the following steps: First, a stereo signal sampled at 44.1 kHz was low-pass filtered with cut-off frequency at 11.025 kHz (half the Nyquist frequency). This was used for an input signal to the proposed system. The frequency shift amount f, was chosen to be 5.5125 kHz. Therefore, the bandwidth of the output signal was set to about 16 kHz. We implemented the high-pass filters with an IIR structure. 9. Modifications The preferred embodiments can be modified while retaining one or more of the features of adaptive high frequency signal level estimation, stereo bandwidth expansion with a common signal, cut-off frequency estimation with spectral curve fits, and bass expansion with both fundamental frequency illusion and frequency band equalization. For example, the number of samples summed for the ratios defining the left and right channel gains can be varied from a few to thousands, the shift frequency can be roughly a target frequency (e.g., 20 kHz)—the cutoff frequency, the interpolation frequencies and size of averages for the cut-off verification could be varied, and the shape and amount of bass boost could be varied, and so forth. Patent Citations
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